{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Anomaly Detection Using RNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Anomaly detection detects data points in data that does not fit well with the rest of data. In this notebook we demonstrate how to do unsupervised anomaly detection using recurrent nueral network (RNN) model.\n",
    "\n",
    "We used one of the dataset in Numenta Anomaly Benchmark (NAB) ([link](https://github.com/numenta/NAB)) for demo, i.e. NYC taxi passengers dataset, which contains 10320 records, each indicating the total number of taxi passengers in NYC at a corresonponding time spot. We use RNN to learn from 50 previous values, and predict just the 1 next value. The data points whose actual values are distant from predicted values are considered anomalies (distance threshold can be adjusted as needed).\n",
    "  \n",
    "References: \n",
    "* Unsupervised real-time anomaly detection for streaming data ([link](https://www.sciencedirect.com/science/article/pii/S0925231217309864))."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Intialization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* import necesary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "from zoo.common.nncontext import *\n",
    "sc = init_nncontext(\"Anomaly Detection Example\")\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib\n",
    "matplotlib.use('Agg')\n",
    "%pylab inline\n",
    "import seaborn\n",
    "import matplotlib.dates as md\n",
    "from matplotlib import pyplot as plt\n",  
    "\n",  
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* import necessary modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from zoo.pipeline.api.keras.layers import Dense, Dropout, LSTM\n",
    "from zoo.pipeline.api.keras.models import Sequential"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Check"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* read data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    dataset_path = os.getenv(\"ANALYTICS_ZOO_HOME\")+\"/bin/data/NAB/nyc_taxi/nyc_taxi.csv\"\n",
    "    df = pd.read_csv(dataset_path)\n",
    "except Exception as e:\n",
    "    print(\"nyc_taxi.csv doesn't exist\")\n",
    "    print(\"you can run $ANALYTICS_ZOO_HOME/bin/data/NAB/nyc_taxi/get_nyc_taxi.sh to download nyc_taxi.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Understand the data.\n",
    "\n",
    "Each record is in format of (timestamp, value). Timestamps range between 2014-07-01 and 2015-01-31. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10320 entries, 0 to 10319\n",
      "Data columns (total 2 columns):\n",
      "timestamp    10320 non-null object\n",
      "value        10320 non-null int64\n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 161.3+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(df.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    2014-07-01 00:00:00\n",
      "1    2014-07-01 00:30:00\n",
      "2    2014-07-01 01:00:00\n",
      "3    2014-07-01 01:30:00\n",
      "4    2014-07-01 02:00:00\n",
      "5    2014-07-01 02:30:00\n",
      "6    2014-07-01 03:00:00\n",
      "7    2014-07-01 03:30:00\n",
      "8    2014-07-01 04:00:00\n",
      "9    2014-07-01 04:30:00\n",
      "Name: timestamp, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# check the timestamp format and frequence \n",
    "print(df['timestamp'].head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15137.5693798\n"
     ]
    }
   ],
   "source": [
    "# check the mean of passenger number \n",
    "print(df['value'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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sMuCDD/Kzt912wP/9X3j73nvvjU8++QQffvgh5s+fj0033RTbbLMNfvazn+G5555DQ0MD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17m8WNWru2xxxiQM+CqkdoZuOmm4SXgt761Ha1TPKJ78cUnBnsMyJmfWjwreR17bNl5YRDIX1Kt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\nTt79QJvXM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4eB3mRmc6Ym53zOVmnRCqQe54QRcjUApzq4GiCvAUm/n3nUADm3A6kGvhTY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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a057910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# change the type of timestamp column for plotting\n",
    "df['datetime'] = pd.to_datetime(df['timestamp'])\n",
    "\n",
    "# visualisation of anomaly throughout time (viz 1)\n",
    "fig, ax = plt.subplots(figsize=(12, 5))\n",
    "\n",
    "ax.plot(df['datetime'], df['value'], color='blue', linewidth=0.6)\n",
    "ax.set_title('NYC taxi passengers throughout time')\n",
    "\n",
    "plt.xlabel('datetime')\n",
    "plt.xticks(rotation=45) \n",
    "plt.ylabel('The Number of NYC taxi passengers')\n",
    "plt.legend(loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Extracting some useful features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the hours when people are awake (6:00-00:00)\n",
    "df['hours'] = df['datetime'].dt.hour\n",
    "df['awake'] = (((df['hours'] >= 6) & (df['hours'] <= 23)) | (df['hours'] == 0)).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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za7xiHi39X+ABYLv89+fcrT4rgf+IiN6kbyGcI6k3MBJ4IiJ2BZ7I7WZm1oyK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\n/j8U0QdpBDALtAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a0ca390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# creation of 2 distinct categories that seem useful (sleeping time and awake time)\n",
    "df['categories'] = df['awake']\n",
    "\n",
    "a = df.loc[df['categories'] == 0, 'value']\n",
    "b = df.loc[df['categories'] == 1, 'value']\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "a_heights, a_bins = np.histogram(a)\n",
    "b_heights, b_bins = np.histogram(b, bins=a_bins)\n",
    "\n",
    "width = (a_bins[1] - a_bins[0])/6\n",
    "\n",
    "ax.bar(a_bins[:-1], a_heights*100/a.count(), width=width, facecolor='yellow', label='Sleeping time')\n",
    "ax.bar(b_bins[:-1]+width, (b_heights*100/b.count()), width=width, facecolor='red', label ='Awake time')\n",
    "ax.set_title('Histogram of NYC taxi passengers in different categories')\n",
    "\n",
    "plt.xlabel('The number of NYC taxi passengers')\n",
    "plt.ylabel('Record counts')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    1\n",
       "2    0\n",
       "3    0\n",
       "Name: awake, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['awake'].head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2014-07-01 00:00:00\n",
       "1    2014-07-01 00:30:00\n",
       "2    2014-07-01 01:00:00\n",
       "3    2014-07-01 01:30:00\n",
       "Name: timestamp, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['timestamp'].head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above result, we can conclude:\n",
    "- more people take taxi when they are awake"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Standardizing data and spliting them into the train data and the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#select and standardize data\n",
    "data_n = df[['value', 'hours', 'awake']]\n",
    "min_max_scaler = preprocessing.StandardScaler()\n",
    "np_scaled = min_max_scaler.fit_transform(data_n)\n",
    "data_n = pd.DataFrame(np_scaled)\n",
    "\n",
    "#important parameters and train/test size\n",
    "prediction_time = 1 \n",
    "testdatasize = 1000\n",
    "unroll_length = 50\n",
    "testdatacut = testdatasize + unroll_length  + 1\n",
    "\n",
    "#train data\n",
    "x_train = data_n[0:-prediction_time-testdatacut].as_matrix()\n",
    "y_train = data_n[prediction_time:-testdatacut  ][0].as_matrix()\n",
    "\n",
    "#test data\n",
    "x_test = data_n[0-testdatacut:-prediction_time].as_matrix()\n",
    "y_test = data_n[prediction_time-testdatacut:  ][0].as_matrix()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Unroll data: for the data point at index i, create a sequence from i to (i+unroll_length)\n",
    "\n",
    "for example, if unroll_length=5\n",
    "\n",
    "[[1]\n",
    "\n",
    " [2]\n",
    " \n",
    " [3]\n",
    " \n",
    " [4]\n",
    " \n",
    " [5]\n",
    " \n",
    " [6]\n",
    " \n",
    " [7]\n",
    " \n",
    " [8]\n",
    " \n",
    " [9]\n",
    " \n",
    " [10]\n",
    " \n",
    "...\n",
    "\n",
    "]\n",
    "\n",
    "will be unrolled to create following sequences\n",
    "\n",
    "[[ 1,  2,  3,  4,  5]\n",
    "\n",
    " [ 2,  3,  4,  5,  6]\n",
    " \n",
    " [ 3,  4,  5,  6,  7]\n",
    " \n",
    " [ 4,  5,  6,  7,  8]\n",
    "\n",
    " [ 5,  6,  7,  8,  9]\n",
    " \n",
    " [ 6,  7,  8,  9,  10]\n",
    " \n",
    " ...\n",
    " \n",
    " ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('x_train', (9218, 50, 3))\n",
      "('y_train', (9218,))\n",
      "('x_test', (1000, 50, 3))\n",
      "('y_test', (1000,))\n"
     ]
    }
   ],
   "source": [
    "#unroll: create sequence of 50 previous data points for each data points\n",
    "def unroll(data,sequence_length=24):\n",
    "    result = []\n",
    "    for index in range(len(data) - sequence_length):\n",
    "        result.append(data[index: index + sequence_length])\n",
    "    return np.asarray(result)\n",
    "\n",
    "# adapt the datasets for the sequence data shape\n",
    "x_train = unroll(x_train,unroll_length)\n",
    "x_test  = unroll(x_test,unroll_length)\n",
    "y_train = y_train[-x_train.shape[0]:]\n",
    "y_test  = y_test[-x_test.shape[0]:]\n",
    "\n",
    "# see the shape\n",
    "print(\"x_train\", x_train.shape)\n",
    "print(\"y_train\", y_train.shape)\n",
    "print(\"x_test\", x_test.shape)\n",
    "print(\"y_test\", y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Here we show an example of building a RNN network using Analytics Zoo Keras-Style API. \n",
    "There are three LSTM layers and one Dense layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "creating: createZooKerasSequential\n",
      "creating: createZooKerasLSTM\n",
      "creating: createZooKerasDropout\n",
      "creating: createZooKerasLSTM\n",
      "creating: createZooKerasDropout\n",
      "creating: createZooKerasDense\n",
      "creating: createRMSprop\n",
      "creating: createMSECriterion\n"
     ]
    }
   ],
   "source": [
    "# Build the model\n",
    "model = Sequential()\n",
    "\n",
    "model.add(LSTM(\n",
    "    input_shape=(x_train.shape[1], x_train.shape[-1]),\n",
    "    output_dim=20,\n",
    "    return_sequences=True))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(LSTM(\n",
    "    10,\n",
    "    return_sequences=False))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(Dense(\n",
    "    output_dim=1))\n",
    "\n",
    "model.compile(loss='mse', optimizer='rmsprop')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training begins.\n",
      "Training completed.\n",
      "CPU times: user 39.6 ms, sys: 31.4 ms, total: 70.9 ms\n",
      "Wall time: 4min 18s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Train the model\n",
    "print(\"Training begins.\")\n",
    "model.fit(\n",
    "    x_train,\n",
    "    y_train,\n",
    "    batch_size=1024,\n",
    "    nb_epoch=20)\n",
    "print(\"Training completed.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction\n",
    "\n",
    "* BigDL models make inferences based on the given data using model.predict(val_rdd) API. A result of RDD is returned. predict_class returns the predicted points. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create the list of difference between prediction and test data\n",
    "diff=[]\n",
    "ratio=[]\n",
    "predictions = model.predict(x_test)\n",
    "p = predictions.collect()\n",
    "for u in range(len(y_test)):\n",
    "    pr = p[u][0]\n",
    "    ratio.append((y_test[u]/pr)-1)\n",
    "    diff.append(abs(y_test[u]- pr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* plot the prediction and the reality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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A06Zh/lt5HBSL1fM0BMkh0V/Zd2IiLjlcLoT4AfAQ4Jq+pJTPD0itaoRpmu4u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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10aac4490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the predicted values and actual values (for the test data)\n",
    "fig, axs = plt.subplots()\n",
    "\n",
    "axs.plot(p,color='red', label='predicted values')\n",
    "axs.plot(y_test,color='blue', label='actual values')\n",
    "axs.set_title('the predicted values and actual values (for the test data)')\n",
    "\n",
    "plt.xlabel('test data index')\n",
    "plt.ylabel('number of taxi passengers after min_max_scalar')\n",
    "plt.legend(loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Set the distance thresold for anomalies. There're many ways to select this threshold. Here we set the expected proportion of anomalies among the entire set. Then we set the threshold as the minimum value of top N distances (here N is the total number of anomalies, i.e. anomaly fraction * total no. of samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# An estimation of anomly population of the dataset\n",
    "outliers_fraction = 0.01\n",
    "# select the most distant prediction/reality data points as anomalies\n",
    "diff = pd.Series(diff)\n",
    "number_of_outliers = int(outliers_fraction*len(diff))\n",
    "threshold = diff.nlargest(number_of_outliers).min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* plot anomalies in the test data throughout time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Y7YaPp/AuD2sBYpSNdhEV20bTHYNluU1KDcMwmkloqQCzVBjloy1ERTSIWCoi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\nzr3ieQlJEhXxwjXeBXJcVPjOo/x+110Hl1xSvb6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UatVU5H3JVPtVccghlTUVeWW/Jokf\nm9VUVMPMmS3Ll53ilmdUuP/+4bfc13uW7L2sRVORpZJQkRYQX321OE+8h1FTUcloMutePY+8Tub2\n21t2DNUa2FXbydWT9PDHuHFwzDFhu5SmopQK3iz49sg+g7FNdtwRvvKVtnXMpUg/Lx/8YMFOKE9T\nkRaIhw+v7vz10FTE56+1NiUuVDQWDd2cZnZB8ntqznJae5evXlQjVGzcWDwuvMce1Z075k93etW8\ngG6+Ofj/T3c61WoqYpTR9At1990rX7N//9DpZTvohQvhQx8qf814zIQJla8TydanmpdjqTzZe/rp\nT4ffPKHiT38qzhs1L9WO6UfVeDmhIqsNKXX+cmPh6c67tR1H1ki1Fmod/sgKa1myQ29t8fFSLen7\n26NHy8irUajYurUgVPzyl/CJT1R3/vjctWX4IWp1ytnolGN729o425aGFioknVtuae/y1YtqhIp7\n7w0v2X32CdsLFlR37rwvmWqEiqOPDi+89Au9Gk3FgAGF9SiQSKXtOdLEcmWn/PXqVf6LOv0VXcsL\nLitUxGPPOaeQdvnlxXlKjXVnX+rZ2AnlbBZqFSpi/nJDPW++2XKYKK/jKSdUlBpmiGQ9M+ZRrfCb\nRzY6bqRUuaImoxTZNmiLi/tqSZe1Z8+WDr3SodGjkDhxYngWqxmiPOig8FvuP718eSFfHvE5ysbq\nqRbXVDQWjd6cOyfLSGAGIaDYAMKskEPbsVx1JU+ouOKKli+9v/0tRNu88cYwVbEaYkeZ7nirESrS\natlINZqKWbMK6/HYvn3D7Iezzy5/zWhTkO1c41z8SPblV8mBUCnyhkrM4LvfLWyfd17x/lKOgbId\nX9zO01RkiZ1ntUJFNUHHok+NcmWE8rYSlSz6Z89uKXRlaW1HlaYaTcV774X4LOXIaia2h1CRnZGS\ndT2eJ1TEdpo7F8aPL3/+uL/cf3rkSHj0UXj++fz9sQxph2qlWLsWTjmlOM2FisaioZvTzC40swuB\nvYBDzew8MzsPGAEMLH905yFPqDj33PygQ716wec+V7vKsdb88UWRF9QsTbbsaSEkrvfpE9anTy9/\nzVtuKaw3N+eXB8JQyp13wt13h+3Wfg23xrVwVoW+dGmwA8ne37hdSag4/vi2D3/ceWfLPHH4I90e\necLXyy+Xvs7s2eXLceyxxZ1mnt3F4MFhKmUlx1I//znsvXf+vmqEive9r6DFK0W2DVoTN6dWdtih\n8Cz07QujRoX1zyRm5/H+pT2F5kUGBhgzJriiTxOH2bJCRTqMfNw/ZEh4ZtatK6Qfd1xhBlPe/+jj\nHw+OtiL77Qcf+UhxHhcqGouu0pz9gfTr/O9JWkOQftkPHFgIZf7QQ8WeJOtFdkpjmgMOKN5OuzRO\nv4wiWaEiL15J7ASywxpZBg8urO+7b7EdRXxxTZoEv/51UHUfdRR84xtw663lz1tqfLrc9NbsfSh1\nzPjxIRrrySfn548d/803wwknwGWXFe/v2bM6oWLRouJjoKCpOOig4Awp3eHEGBfZDiFL3vTVH/4w\nDK/lee2M3HZb+LKNneL48bBhQwiElvYv0rNnsCGJHiIBrryy5fkOPzz438jSvXtLYagWnwgzZxbW\ns0JFU1P152kL8Rno0ye01ZYtBe+esS6bNxfud55r/P79w7sg7aPjU58qCFJZAemss0qXJwog/fqF\ndpw3D1auhAMPbJl3jz0KRtCRrJZl6NDS13I6H11FqPgJ8IikuZLmAsuAH1VzoKRJktZIapZ0fs7+\nnpIWJvuXSRqc2jcnSV8jaWJdapLDkUcWpr717l14UeyzT5j2Fr8A0y/mtlBOqHjkkWKfCmkr9C9/\nOdhapIkvqBEjwhj7lCmFffGlM25c+K0kVGRZtqzQ0UShYsaMwhetBF/9KgwaVHzc/PnFnXwpV+Wj\nR5e+9u9/D088EdbXrCk4FSr1dXvGGcWanOjo6Ljjwu9hhwXBYPLk4uN69SocF+9P796h87juuqBu\nfv75YnuBG24Iv1Go6NYt1GXGjKBd2LgxuIiG8AzNnFk+QFmWAw6AqVPL54n1ip2iFNp7woSW7ZEu\nK4SO6vLL4a67Cs9IKUPDPAEka1CYFj4jcaju6qsLaVGoOOGEIGBdemn+NbcV0b4iLTScdVa4l2ee\nCddeGzQWaa1XvC9XX10QgpYsCb9pzc5rrxVfq9ywVjr4XSzP8OGFch14YMFFep4xcCzTtGnh/1BK\nw+R0ThraT0XEzL4l6TcE75oAp5rZ45WOk9QNuBo4BlgHLJe02MzSM9ZPBzaa2X6STgQuAaZIGgac\nCBwI7AncLWl/M6shqkL1ZNXlaTZsgPvvLz2+OnVqsLOIjB4dOuQsJ50UvrTzZgY89lh4yfTpU6xe\nPvLIwvrAgaEjGDWq4FVz+PDQ4U2e3PJLu6kpdM4jRoTtUtM9b7kF1q9vmb7zzoUXcS1T5k47LSw3\n3xxe0jvsECJ/RpWzFDQGeS6rI7vuWujk998fXnklrJcyOI2h0089NfgjGDQouFnPznrJfnXvtltB\nUNlpp9A2739/S5VyWj0efY5kY7f07NlSEyIVd6yVuPjioGavlqgKzwq8a9YU2+CkhYoRIwp+MWIH\nVWooKm/IrHfvcL4HHggdcTrmya23hlkghx8Oj2feEPFaM2YUVP7bk7z/3W67FbuSzwoD8T+VFjQm\nTgwzotIauOzzEp+zvCHU+D/MtnPUWvXpU/59FNsq63HXaRDMzJcSCzAWuCO1PQeYk8lzBzA2We8O\nvAYomzedr9QyYsQIay2vvBKCSJ97bvXHjBlj9q1vmb3zjtkLL8Qg1GabNoXfwYPzj/vd7wp5hw41\ne+yx8teJeSPr14drg9nFF1df3vS5wOzkk4vPW46FC0Pe5curv9bKlWazZplt3Rq2+/cP53jvvbDd\n3GzWr5/ZF75gNndu+XNt3Wr25JOFY1vLu++a9ehhdt11ZhddZPbWW2YPP2w2bVr5c2/ZEso+b14h\nbfFis+HDwzlr4etfL7TBrFnhd9Eis+9/v2XebNuD2ZAhxXk2bqx8X666Khx7xRXF6fH6mzebvfaa\n2dixZgsWtLxuPXjpJbM5c8K93J7ceGOoyy9+Ufuxr78eylypjRcvNps61Wz2bLOnngppq1aF/0Ae\nK1aY/fWvxWnPPhvKeffdZjfdFNa/9rWWx27caHbEEWZ/+EPN1SkJsMI6QJ/hi6HQHk4ekiYDk8zs\njGR7GjDazL6YyrMqybMu2X4OGA3MBR42swVJ+nzgN2a2KHON6cB0gIEDB474U9YJQQ2sWxeCG7VW\n+l+9OnzxH3FE0G7svHP5L/zm5vBFXWmO+4oV4Wt67NhC2ubNwTp97tzavC0uWRK+voYODV9Tb70V\nDNiqYdOmtsVn+OMf4cEHK6v2Oypm9fMJcN99QYvS1BRsHkqF/F6yJLRPbPtNm/KjwVZiy5YQAOuM\nM4qf761bwzOQnSb69tvhmas0rbWzsHx5mIXR0X06xGfMLISlnzx52zgFyyLpUTMbue2v5FTChYoy\nbA+hIs3IkSNtRSU/1I7jOE4RLlR0HLqKoWZreRFImxHtlaTl5pHUHegLbKjyWMdxHMdpGFyoKM9y\noEnSEEk9CIaXizN5FgPRnctk4N5kjG8xcGIyO2QI0ATUYEPvOI7jOJ0Lt70tg5ltkfRFgpFl5qPI\nXQAACBdJREFUN+AGM3tK0jyCYdBiYD7wU0nNwOsEwYMk3y+Ap4EtwJm2jWZ+OI7jOE5HwG0qOhBu\nU+E4jlM7blPRcfDhD8dxHMdx6oILFY7jOI7j1AUXKhzHcRzHqQsuVDiO4ziOUxfcULMDIWk90FqX\nmrsRXIR3JbzOXQOvc9egLXUeZGa7V87mbGtcqGgQJK3oatbPXueugde5a9AV69yI+PCH4ziO4zh1\nwYUKx3Ecx3HqggsVjcMP2rsA7YDXuWvgde4adMU6NxxuU+E4juM4Tl1wTYXjOI7jOHXBhQrHcRzH\nceqCCxUNgKRJktZIapZ0fnuXp15I2lvSUklPS3pK0jlJ+q6S7pK0Nvntl6RL0veS+/CEpEPbtwat\nQ1I3SY9Lui3ZHiJpWVKvhZJ6JOk9k+3mZP/g9ix3a5G0i6RFklZLekbS2C7QxrOSZ3qVpJ9J6tVo\n7SzpBkmvSlqVSqu5XSWdkuRfK+mU9qiLUz0uVHRyJHUDrgY+BgwDTpI0rH1LVTe2AOeZ2TBgDHBm\nUrfzgXvMrAm4J9mGcA+akmU6cO32L3JdOAd4JrV9CfAdM9sP2AicnqSfDmxM0r+T5OuMXAksMbOh\nwHBC3Ru2jSUNAM4GRprZQUA34EQar51/BEzKpNXUrpJ2BS4ARgOjgAuiIOJ0TFyo6PyMAprN7Hkz\n+zvwc+CT7VymumBmL5vZY8n6m4TOZgChfj9Osv0Y+FSy/kngJxZ4GNhF0h7budhtQtJewHHA9cm2\ngKOARUmWbH3jfVgEfDTJ32mQ1BcYB8wHMLO/m9kmGriNE7oD75fUHdgReJkGa2czux94PZNca7tO\nBO4ys9fNbCNwFy0FFacD4UJF52cA8OfU9rokraFIVL6HAMuA/mb2crLrL0D/ZL0R7sV3gX8Dtibb\nHwA2mdmWZDtdp3/UN9n/RpK/MzEEWA/8MBnyuV7STjRwG5vZi8DlwAsEYeIN4FEau50jtbZrp2/v\nroYLFU6HR1Jv4FfAl8zsr+l9FuZEN8S8aEkfB141s0fbuyzbke7AocC1ZnYI8BYFlTjQWG0MkKjv\nP0kQqPYEdqILfn03Wrs6ARcqOj8vAnuntvdK0hoCSTsQBIobzeymJPmVqPJOfl9N0jv7vTgMOF7S\nHwnDWEcR7A12SdTkUFynf9Q32d8X2LA9C1wH1gHrzGxZsr2IIGQ0ahs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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b8091d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the difference and the threshold (for the test data)\n",
    "fig, axs = plt.subplots()\n",
    "\n",
    "axs.plot(diff,color='blue', label='diff')\n",
    "axs.set_title('the difference between the predicted values and actual values with the threshold line')\n",
    "\n",
    "plt.hlines(threshold, 0, 1000, color='red', label='threshold')\n",
    "plt.xlabel('test data index')\n",
    "plt.ylabel('difference value after min_max_scalar')\n",
    "plt.legend(loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data with anomaly label (test data part)\n",
    "test = (diff >= threshold).astype(int)\n",
    "# the training data part where we didn't predict anything (overfitting possible): no anomaly\n",
    "complement = pd.Series(0, index=np.arange(len(data_n)-testdatasize))\n",
    "last_train_data= (df['datetime'].tolist())[-testdatasize]\n",
    "# add the data to the main\n",
    "df['anomaly27'] = complement.append(test, ignore_index='True')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* plot anomalies in the test data throughout time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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aCtv96U9/4qSTTuLRRx/l6KOPLp9/7rnnsmDBAvbZZx+MMbRo0YIXXngh9vEr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riuqK/Xa/L6eS40YyVKjMHMIbds+etf320DKyA++ujmJVIs62DQfDm7FiRbqyy567B+XP4lQYqA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\nt10qyLWouHxTJz9O1jMrb1N5XtKGWv2c5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7JSmp/adeWm/aWEPKfN3QotHdKGzJ9cOwGM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\nprQvr4dHyOPcUc13pYLcEgrWYEumwxIEcrAlr8PK29KBbz/CO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      "text/plain": [
       "<matplotlib.figure.Figure at 0x104e99950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualisation of anomaly throughout time (viz 1)\n",
    "fig, ax = plt.subplots(figsize=(12, 5))\n",
    "\n",
    "a = df.loc[df['anomaly27'] == 1, ['datetime', 'value']] #anomaly\n",
    "ax.plot(df['datetime'], df['value'], color='blue', label='no anomaly value', linewidth=0.6)\n",
    "ax.scatter(a['datetime'].tolist(),a['value'], color='red', label='anomalies value')\n",
    "ax.set_title('the number of nyc taxi value throughout time (with anomalies scattered)')\n",
    "\n",
    "max_value = df['value'].max()\n",
    "min_value = df['value'].min()\n",
    "plt.vlines(last_train_data, min_value, max_value, color='black', linestyles = \"dashed\", label='test begins')\n",
    "plt.xlabel('datetime')\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylabel('the number of nyc taxi value')\n",
    "plt.legend(loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
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  "toc": {
   "base_numbering": 1,
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   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
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   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
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